Designers of imaging systems often assess the performance of their designs in terms of physical parameters such as contrast, resolution and bit-rate efficiency in compression/decompression (codec) processes. While these parameters can be easily measured, they may not be accurate gauges for evaluating performance. The reason is that end users of imaging systems are generally more concerned with the subjective visual performance such as the visibility of artifacts or distortions and in some cases, the enhancement of these image features which may reveal information such as the existence of a tumor in an image, e.g., a MRI (Magnetic Resonance Imaging) image or a CAT (Computer-Assisted Tomography) scan image.
For example, an input image can be processed using two different codec algorithms to produce two different codec images. If the measure of codec image fidelity is based purely on parameters such as performing mean squared error (MSE) calculations on both codec images without considering the psychophysical properties of human vision, the codec image with a lower MSE value may actually contain more noticeable distortions than that of a codec image with a higher MSE value.
Over the years, various human visual performance models have been used to improve imaging system design. One model (known as the Carlson and Cohen model) decomposes an input image by partitioning its one-dimensional power spectrum into a number of discrete adjacent frequency bands. The integral of the amplitude values within each band is then subjected to a static non-linearity that is accelerating for small input values and compressive for large values. Changes in the output of this process from one member of a pair of images to the other provide a simple perceptual measure of the visibility of differences between the two images.
A similar method is the square root integral model (SQRI). In this model, the separate frequency-selective bands are replaced by a single integral over spatial frequencies, based on the ratio between the modulation transfer function of the display and an arithmetic approximation to the contrast sensitivity function of the human observer. Although the SQRI has been successfully applied to a number of different display evaluation problems, this model and other basic psychophysics models are spatially one-dimensional. Namely, these models predict sensitivity to spatial variation in one dimension only.
Therefore, a need exists in the art for a method and apparatus for assessing the effects of physical parameters on the subjective visual performance of an imaging system. Specifically, a need exists for a method and apparatus for assessing the visibility of differences between two sequences of time-varying visual images.